mirror of
https://github.com/open-mmlab/mmselfsup.git
synced 2025-06-03 14:59:38 +08:00
parent
c176fdad6e
commit
3c51e86d44
@ -94,7 +94,8 @@ class TestDenseCLHook(TestCase):
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custom_hooks=[densecl_hook],
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custom_hooks=[densecl_hook],
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default_hooks=dict(logger=None),
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default_hooks=dict(logger=None),
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log_processor=dict(window_size=1),
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log_processor=dict(window_size=1),
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experiment_name='test_densecl_hook')
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experiment_name='test_densecl_hook',
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default_scope='mmselfsup')
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runner.train()
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runner.train()
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@ -104,7 +104,8 @@ class TestSimSiamHook(TestCase):
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custom_hooks=[simsiam_hook],
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custom_hooks=[simsiam_hook],
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default_hooks=dict(logger=None),
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default_hooks=dict(logger=None),
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log_processor=dict(window_size=1),
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log_processor=dict(window_size=1),
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experiment_name='test_simsiam_hook')
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experiment_name='test_simsiam_hook',
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default_scope='mmselfsup')
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runner.train()
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runner.train()
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@ -107,7 +107,8 @@ class TestSwAVHook(TestCase):
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custom_hooks=[swav_hook],
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custom_hooks=[swav_hook],
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default_hooks=dict(logger=None),
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default_hooks=dict(logger=None),
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log_processor=dict(window_size=1),
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log_processor=dict(window_size=1),
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experiment_name='test_swav_hook')
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experiment_name='test_swav_hook',
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default_scope='mmselfsup')
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runner.train()
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runner.train()
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@ -7,6 +7,9 @@ import torch
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from mmselfsup.models.algorithms.barlowtwins import BarlowTwins
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from mmselfsup.models.algorithms.barlowtwins import BarlowTwins
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.utils import register_all_modules
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register_all_modules()
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backbone = dict(
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backbone = dict(
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type='ResNet',
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type='ResNet',
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@ -7,7 +7,9 @@ import torch
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from mmselfsup.models.algorithms.byol import BYOL
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from mmselfsup.models.algorithms.byol import BYOL
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.utils import register_all_modules
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register_all_modules()
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backbone = dict(
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backbone = dict(
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type='ResNet',
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type='ResNet',
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depth=18,
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depth=18,
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@ -7,6 +7,9 @@ from mmengine.data import InstanceData
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from mmselfsup.models.algorithms import CAE
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from mmselfsup.models.algorithms import CAE
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.utils import register_all_modules
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register_all_modules()
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# model settings
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# model settings
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backbone = dict(type='CAEViT', arch='b', patch_size=16, init_values=0.1)
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backbone = dict(type='CAEViT', arch='b', patch_size=16, init_values=0.1)
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@ -8,6 +8,9 @@ from mmengine.data import InstanceData
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from mmselfsup.models.algorithms import DeepCluster
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from mmselfsup.models.algorithms import DeepCluster
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.utils import register_all_modules
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register_all_modules()
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num_classes = 5
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num_classes = 5
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with_sobel = True,
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with_sobel = True,
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@ -9,6 +9,9 @@ import torch
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import mmselfsup
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import mmselfsup
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from mmselfsup.models.algorithms.densecl import DenseCL
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from mmselfsup.models.algorithms.densecl import DenseCL
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.utils import register_all_modules
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register_all_modules()
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queue_len = 32
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queue_len = 32
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feat_dim = 2
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feat_dim = 2
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@ -7,6 +7,9 @@ import torch
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from mmselfsup.models.algorithms.mae import MAE
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from mmselfsup.models.algorithms.mae import MAE
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.utils import register_all_modules
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register_all_modules()
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backbone = dict(type='MAEViT', arch='b', patch_size=16, mask_ratio=0.75)
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backbone = dict(type='MAEViT', arch='b', patch_size=16, mask_ratio=0.75)
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neck = dict(
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neck = dict(
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@ -7,6 +7,9 @@ import torch
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from mmselfsup.models.algorithms import MoCo
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from mmselfsup.models.algorithms import MoCo
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.utils import register_all_modules
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register_all_modules()
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queue_len = 32
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queue_len = 32
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feat_dim = 2
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feat_dim = 2
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@ -7,6 +7,9 @@ import torch
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from mmselfsup.models import MoCoV3
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from mmselfsup.models import MoCoV3
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.utils import register_all_modules
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register_all_modules()
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backbone = dict(
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backbone = dict(
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type='MoCoV3ViT',
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type='MoCoV3ViT',
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@ -8,6 +8,9 @@ from mmengine.data import InstanceData
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from mmselfsup.models.algorithms import NPID
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from mmselfsup.models.algorithms import NPID
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.utils import register_all_modules
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register_all_modules()
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backbone = dict(
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backbone = dict(
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type='ResNet',
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type='ResNet',
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@ -8,6 +8,9 @@ from mmengine.data import InstanceData
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from mmselfsup.models.algorithms import ODC
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from mmselfsup.models.algorithms import ODC
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.utils import register_all_modules
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register_all_modules()
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num_classes = 5
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num_classes = 5
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backbone = dict(
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backbone = dict(
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@ -7,6 +7,9 @@ from mmengine.data import InstanceData
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from mmselfsup.models.algorithms.relative_loc import RelativeLoc
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from mmselfsup.models.algorithms.relative_loc import RelativeLoc
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.utils import register_all_modules
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register_all_modules()
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backbone = dict(
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backbone = dict(
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type='ResNet',
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type='ResNet',
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@ -8,6 +8,9 @@ from mmengine.data import InstanceData
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from mmselfsup.models.algorithms.rotation_pred import RotationPred
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from mmselfsup.models.algorithms.rotation_pred import RotationPred
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.utils import register_all_modules
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register_all_modules()
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backbone = dict(
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backbone = dict(
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type='ResNet',
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type='ResNet',
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@ -7,6 +7,9 @@ import torch
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from mmselfsup.models.algorithms.simclr import SimCLR
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from mmselfsup.models.algorithms.simclr import SimCLR
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.utils import register_all_modules
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register_all_modules()
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backbone = dict(
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backbone = dict(
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type='ResNet',
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type='ResNet',
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@ -7,6 +7,9 @@ from mmengine.data import InstanceData
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from mmselfsup.models.algorithms import SimMIM
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from mmselfsup.models.algorithms import SimMIM
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.utils import register_all_modules
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register_all_modules()
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@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
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@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
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@ -7,6 +7,9 @@ import torch
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from mmselfsup.models.algorithms import SimSiam
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from mmselfsup.models.algorithms import SimSiam
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.utils import register_all_modules
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register_all_modules()
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backbone = dict(
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backbone = dict(
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type='ResNet',
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type='ResNet',
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@ -7,6 +7,9 @@ import torch
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from mmselfsup.models.algorithms.swav import SwAV
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from mmselfsup.models.algorithms.swav import SwAV
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.utils import register_all_modules
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register_all_modules()
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nmb_crops = [2, 6]
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nmb_crops = [2, 6]
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backbone = dict(
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backbone = dict(
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